Exercise

# The Regression Coefficients II

With both `smile`

and `money`

in your model, you found that the slope coefficient for `money`

is 0.8008, `smile`

is 1.4895 and the intercept is 0.6162. How is this different from predicting `liking`

with **only** `smile`

or **only** `money`

?

In your console, look at the coefficients for a model with only `smile`

by entering `lm(liking ~ smile)`

and only `money`

by entering `lm(liking ~ money)`

. Compare the change in coefficients from the model with two predictors, to the model with one predictor and select the correct statment from below.

Instructions

**50 XP**

##### Possible Answers

- Both money and smiling are stronger predictors of liking when they are included in the model together.
- Both money and smiling are stronger predictors of liking individually in the one-predictor models, compared to in the model with two predictors.
- Money is a stronger predictor in the model with two predictors, smiling is stronger in the model with one predictor.
- While the intercept stays at the same value, both predictors change in the model containing two predictors compared to the models with one predictor.